{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 行为克隆\n",
    "\n",
    "behavior cloning，BC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gymnasium as gym\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import rl_utils\n",
    "import numpy as np\n",
    "import random\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PolicyNet(torch.nn.Module):\n",
    "    def __init__(self, state_dim, hidden_dim, action_dim):\n",
    "        super(PolicyNet, self).__init__()\n",
    "        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)\n",
    "        self.fc2 = torch.nn.Linear(hidden_dim, action_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        return F.softmax(self.fc2(x), dim=-1)\n",
    "\n",
    "class ValueNet(torch.nn.Module):\n",
    "    def __init__(self, state_dim, hidden_dim):\n",
    "        super().__init__()\n",
    "        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)\n",
    "        self.fc2 = torch.nn.Linear(hidden_dim, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        return self.fc2(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PPO算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PPO:\n",
    "    ''' PPO算法, 采用截断方式 '''\n",
    "    def __init__(self, state_dim, hidden_dim, action_dim, actor_lr, critic_lr,\n",
    "                 lmbda, epochs, eps, gamma, device):\n",
    "        self.actor = PolicyNet(state_dim, hidden_dim, action_dim).to(device)\n",
    "        self.critic = ValueNet(state_dim, hidden_dim).to(device)\n",
    "        self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=actor_lr)\n",
    "        self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=critic_lr)\n",
    "        self.gamma = gamma\n",
    "        self.lmbda = lmbda\n",
    "        self.epochs = epochs  # 一条序列的数据用于训练轮数\n",
    "        self.eps = eps  # PPO中截断范围的参数\n",
    "        self.device = device\n",
    "\n",
    "    def take_action(self, state):\n",
    "        state = torch.tensor(np.array(state), dtype=torch.float).to(self.device)\n",
    "        probs = self.actor(state)\n",
    "        action_dist = torch.distributions.Categorical(probs)\n",
    "        action = action_dist.sample()\n",
    "        return action.item()\n",
    "\n",
    "    def update(self, transition_dict):\n",
    "        states = torch.tensor(transition_dict['states'], dtype=torch.float).to(self.device)\n",
    "        actions = torch.tensor(transition_dict['actions']).view(-1, 1).to(self.device)\n",
    "        rewards = torch.tensor(transition_dict['rewards'], dtype=torch.float).view(-1, 1).to(self.device)\n",
    "        next_states = torch.tensor(transition_dict['next_states'], dtype=torch.float).to(self.device)\n",
    "        dones = torch.tensor(transition_dict['dones'], dtype=torch.int).view(-1, 1).to(self.device)\n",
    "        truncated = torch.tensor(transition_dict['truncated'], dtype=torch.int).view(-1, 1).to(self.device)\n",
    "        td_target = rewards + self.gamma * self.critic(next_states) * (1 - dones | truncated)\n",
    "        td_delta = td_target - self.critic(states)\n",
    "        advantage = rl_utils.compute_advantage(self.gamma, self.lmbda, td_delta.cpu()).to(self.device)\n",
    "        old_log_probs = torch.log(self.actor(states).gather(1, actions)).detach()\n",
    "\n",
    "        for _ in range(self.epochs):\n",
    "            log_probs = torch.log(self.actor(states).gather(1, actions))\n",
    "            ratio = torch.exp(log_probs - old_log_probs)\n",
    "            surr1 = ratio * advantage\n",
    "            surr2 = torch.clamp(ratio, 1 - self.eps, 1 + self.eps) * advantage  # 截断\n",
    "            actor_loss = torch.mean(-torch.min(surr1, surr2))  # PPO损失函数\n",
    "            critic_loss = torch.mean(F.mse_loss(self.critic(states), td_target.detach()))\n",
    "            self.actor_optimizer.zero_grad()\n",
    "            self.critic_optimizer.zero_grad()\n",
    "            actor_loss.backward()\n",
    "            critic_loss.backward()\n",
    "            self.actor_optimizer.step()\n",
    "            self.critic_optimizer.step()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取专家模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "device: cuda\n"
     ]
    }
   ],
   "source": [
    "# PPO相关\n",
    "actor_lr = 1e-3\n",
    "critic_lr = 1e-2\n",
    "lmbda = 0.95  # 似乎可以去掉，这一项仅用于调整计算优势advantage时，额外调整折算奖励的系数\n",
    "gamma = 0.98  # 时序差分学习率，也作为折算奖励的系数之一\n",
    "total_epochs = 10  # 迭代轮数\n",
    "total_episodes = 500  # 一轮训练多少次游戏\n",
    "\n",
    "eps = 0.2  # 截断范围参数, 1-eps ~ 1+eps\n",
    "epochs = 10  # PPO中一条序列训练多少轮，和迭代算法无关\n",
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print('device:', device)\n",
    "\n",
    "# 环境相关\n",
    "env_name = 'CartPole-v1'\n",
    "env = gym.make(env_name)\n",
    "torch.manual_seed(0)\n",
    "\n",
    "# 神经网络相关\n",
    "hidden_dim = 128\n",
    "state_dim = env.observation_space.shape[0]\n",
    "action_dim = env.action_space.n\n",
    "\n",
    "\n",
    "agent = PPO(state_dim, hidden_dim, action_dim, actor_lr, critic_lr, gamma, lmbda, epochs, eps, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ckp_path = 'checkpoints/PPO_CPv1.pt'\n",
    "checkpoint = torch.load(ckp_path)\n",
    "agent.actor.load_state_dict(checkpoint['actor_best_weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_expert_data(n_episode):\n",
    "    states = []\n",
    "    actions = []\n",
    "    for _ in range(n_episode):\n",
    "        state = env.reset()[0]\n",
    "        done = truncated = False\n",
    "        while not done:\n",
    "            action = agent.take_action(state)\n",
    "            states.append(state)\n",
    "            actions.append(action)\n",
    "            next_state, reward, done, truncated, _ = env.step(action)\n",
    "            state = next_state\n",
    "    return np.array(states), np.array(actions)\n",
    "\n",
    "env = gym.make('CartPole-v1', render_mode='rgb_array')\n",
    "torch.manual_seed(0)\n",
    "random.seed(0)\n",
    "n_episode = 1\n",
    "expert_s, expert_a = sample_expert_data(n_episode)  # 收集专家数据\n",
    "\n",
    "n_samples = 500  # 采样数据\n",
    "random_index = random.sample(range(expert_s.shape[0]), n_samples)\n",
    "expert_s = expert_s[random_index]\n",
    "expert_a = expert_a[random_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行为克隆"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                           \r"
     ]
    }
   ],
   "source": [
    "class BehaviorClone:\n",
    "    def __init__(self, state_dim, hidden_dim, action_dim, lr):\n",
    "        self.policy = PolicyNet(state_dim, hidden_dim, action_dim).to(device)  # 只用训练决策网络\n",
    "        self.optimizer = torch.optim.Adam(self.policy.parameters(), lr=lr)\n",
    "\n",
    "    def learn(self, states, actions):\n",
    "        states = torch.tensor(states, dtype=torch.float).to(device)\n",
    "        actions = torch.tensor(actions, dtype=torch.int64).view(-1, 1).to(device)\n",
    "        log_probs = torch.log(self.policy(states).gather(1, actions))\n",
    "        bc_loss = torch.mean(-log_probs)  # 最大似然估计\n",
    "\n",
    "        self.optimizer.zero_grad()\n",
    "        bc_loss.backward()\n",
    "        self.optimizer.step()\n",
    "\n",
    "    def take_action(self, state):\n",
    "        state = torch.tensor(state[np.newaxis, :], dtype=torch.float).to(device)\n",
    "        probs = self.policy(state)\n",
    "        action_dist = torch.distributions.Categorical(probs)\n",
    "        action = action_dist.sample()\n",
    "        return action.item()\n",
    "\n",
    "\n",
    "def test_agent(agent, env, n_episode):\n",
    "    return_list = []\n",
    "    for episode in range(n_episode):\n",
    "        episode_return = 0\n",
    "        state = env.reset()[0]\n",
    "        done = truncated = False\n",
    "        while not done:\n",
    "            action = agent.take_action(state)\n",
    "            next_state, reward, done, truncated, _ = env.step(action)\n",
    "            state = next_state\n",
    "            episode_return += reward\n",
    "        return_list.append(episode_return)\n",
    "    return np.mean(return_list)\n",
    "\n",
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "\n",
    "lr = 1e-3\n",
    "bc_agent = BehaviorClone(state_dim, hidden_dim, action_dim, lr)\n",
    "n_iterations = 1000\n",
    "batch_size = 64\n",
    "test_returns = []\n",
    "\n",
    "with tqdm(total=n_iterations, desc=\"进度条\", leave=False) as pbar:\n",
    "    for i in range(n_iterations):\n",
    "        # 创建一个乱序\n",
    "        sample_indices = np.random.randint(low=0, high=expert_s.shape[0], size=batch_size)\n",
    "        bc_agent.learn(expert_s[sample_indices], expert_a[sample_indices])\n",
    "        current_return = test_agent(bc_agent, env, 5)\n",
    "        # 收集reward\n",
    "        test_returns.append(current_return)\n",
    "        if (i + 1) % 10 == 0:\n",
    "            pbar.set_postfix({'return': '%.3f' % np.mean(test_returns[-10:])})\n",
    "        pbar.update(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rl_utils.picture_return(test_returns, 'PPO_BC', env_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 评价\n",
    "\n",
    "行为克隆效果一般不会很好，因为每次环境反馈的不会完全一样，只有在专家碰到过的那个行为分布上，才会有比较好的效果，一旦碰到专家没碰到过的情况，智能体就只能随机选择动作，可能会有比较大的错误，这种错误会一直积累，所以泛化性很差。\n",
    "\n",
    "用一个专家轨迹作为labels，训练方法和深度学习一样，专家可以是人，也可以是训练好的模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 生成对抗模仿学习\n",
    "\n",
    "generative adversarial imitation learning，GAIL\n",
    "\n",
    "前面还是要写PPO算法并且定义agent, 还要生成专家数据，存储专家状态和动作表\n",
    "\n",
    "和BC的区别是agent自己和环境交互"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 判别器网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Discriminator(torch.nn.Module):\n",
    "    def __init__(self, state_dim, hidden_dim, action_dim):\n",
    "        super().__init__()\n",
    "        self.fc1 = torch.nn.Linear(state_dim + action_dim, hidden_dim)\n",
    "        self.fc2 = torch.nn.Linear(hidden_dim, 1)\n",
    "\n",
    "    def forward(self, x, a):\n",
    "        cat = torch.cat([x, a], dim=1)\n",
    "        x = F.relu(self.fc1(cat))\n",
    "        # 输出的是概率\n",
    "        return torch.sigmoid(self.fc2(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GAIL算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": ""
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "进度条: 100%|██████████| 500/500 [03:47<00:00,  2.20it/s, return=500.000]\n"
     ]
    }
   ],
   "source": [
    "class GAIL:\n",
    "    def __init__(self, agent, state_dim, action_dim, hidden_dim, lr_d):\n",
    "        self.discriminator = Discriminator(state_dim, hidden_dim, action_dim).to(device)\n",
    "        self.discriminator_optimizer = torch.optim.Adam(self.discriminator.parameters(), lr=lr_d)\n",
    "        self.agent = agent\n",
    "\n",
    "    def learn(self, expert_s, expert_a, agent_s, agent_a, next_s, dones, truncated):\n",
    "        expert_states = torch.tensor(np.array(expert_s), dtype=torch.float).to(device)\n",
    "        expert_actions = torch.tensor(np.array(expert_a), dtype=torch.int64).to(device)\n",
    "        agent_states = torch.tensor(np.array(agent_s), dtype=torch.float).to(device)\n",
    "        agent_actions = torch.tensor(np.array(agent_a), dtype=torch.int64).to(device)\n",
    "        # 把动作分为两类的独热编码\n",
    "        expert_actions = F.one_hot(expert_actions, num_classes=2).float()\n",
    "        agent_actions = F.one_hot(agent_actions, num_classes=2).float()\n",
    "\n",
    "        expert_prob = self.discriminator(expert_states, expert_actions)  # 专家状态动作打分\n",
    "        agent_prob = self.discriminator(agent_states, agent_actions)  # agent状态动打分\n",
    "        # * 二元交叉熵损失 BCELoss, 专家轨迹label为0, agent轨迹label为1, 越靠近0, 代表该轨迹越接近专家轨迹\n",
    "        # * 这里让目标函数梯度下降, 可以让agent与环境交互的行为分布接近专家的交互分布\n",
    "        # * 也就是让agent生成的状态动作接近轨迹\n",
    "        discriminator_loss = torch.nn.BCELoss()(agent_prob, torch.ones_like(agent_prob)) \\\n",
    "            + torch.nn.BCELoss()(expert_prob, torch.zeros_like(expert_prob))\n",
    "        self.discriminator_optimizer.zero_grad()\n",
    "        discriminator_loss.backward()\n",
    "        self.discriminator_optimizer.step()\n",
    "\n",
    "        # agent_prob是判别器把agent识别为模仿者的概率, 所以可以让这个概率越小越好, 因此就加负号, 变成奖励\n",
    "        # 这样在训练演员的时候, 就可以引导演员往降低agent_prob的方向调整\n",
    "        rewards = -torch.log(agent_prob).detach().cpu().numpy()\n",
    "        transition_dict = {\n",
    "            'states': agent_s,\n",
    "            'actions': agent_a,\n",
    "            'rewards': rewards,\n",
    "            'next_states': next_s,\n",
    "            'truncated': truncated,\n",
    "            'dones': dones\n",
    "        }\n",
    "        self.agent.update(transition_dict)\n",
    "\n",
    "torch.manual_seed(0)\n",
    "lr_d = 1e-3\n",
    "agent = PPO(state_dim, hidden_dim, action_dim, actor_lr, critic_lr, lmbda,\n",
    "            epochs, eps, gamma, device)\n",
    "gail = GAIL(agent, state_dim, action_dim, hidden_dim, lr_d)\n",
    "n_episode = 500\n",
    "return_list = []\n",
    "\n",
    "with tqdm(total=n_episode, desc=\"进度条\") as pbar:\n",
    "    for i in range(n_episode):\n",
    "        episode_return = 0\n",
    "        state = env.reset()[0]\n",
    "        done = truncated = False\n",
    "        state_list = []\n",
    "        action_list = []\n",
    "        next_state_list = []\n",
    "        done_list = []\n",
    "        truncated_list = []\n",
    "        while not (done | truncated):\n",
    "            action = agent.take_action(state)\n",
    "            next_state, reward, done, truncated, _ = env.step(action)\n",
    "            state_list.append(state)\n",
    "            action_list.append(action)\n",
    "            next_state_list.append(next_state)\n",
    "            done_list.append(done)\n",
    "            truncated_list.append(truncated)\n",
    "            state = next_state\n",
    "            episode_return += reward\n",
    "        return_list.append(episode_return)\n",
    "        gail.learn(expert_s, expert_a, state_list, action_list,\n",
    "                   next_state_list, done_list, truncated_list)\n",
    "        if (i + 1) % 10 == 0:\n",
    "            pbar.set_postfix({'return': '%.3f' % np.mean(return_list[-10:])})\n",
    "        pbar.update(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": ""
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rl_utils.picture_return(return_list, 'GAIL', env_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
